Recorded 30 November 2022. Mahdi Soltanolkotabi of the University of Southern California presents "Medical image reconstruction via deep learning: new architectures, data reduction and robustness" at IPAM's Multi-Modal Imaging with Deep Learning and Modeling Workshop. Abstract: In this talk I will discuss the challenges and opportunities for using deep learning in medical image reconstruction. Contemporary techniques in this field rely on convolutional architectures that are limited by the spatial invariance of their filters and have difficulty modeling long-range dependencies. To remedy this, I will discuss our work on designing new transformer-based architectures called HUMUS-Net that lead to state of the art performance and do not suffer from these limitations. In the next part of the talk I will report on techniques to significantly reduce the required data for training. Time permitting I will discuss other exciting directions for the use of deep learning in MR. Learn more online at: http://www.ipam.ucla.edu/programs/workshops/workshop-iv-multi-modal-imaging-with-deep-learning-and-modeling/?tab=overview